Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm

With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value...

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Main Author: Xiaojun Jiang
Format: Article
Language:English
Published: Hindawi-Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/5577167
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spelling doaj-9b992efeefe642dc947607f3e9ac7dc92021-04-12T01:24:06ZengHindawi-WileyComplexity1099-05262021-01-01202110.1155/2021/5577167Online English Teaching Course Score Analysis Based on Decision Tree Mining AlgorithmXiaojun Jiang0School of Translation StudiesWith the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given.http://dx.doi.org/10.1155/2021/5577167
collection DOAJ
language English
format Article
sources DOAJ
author Xiaojun Jiang
spellingShingle Xiaojun Jiang
Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm
Complexity
author_facet Xiaojun Jiang
author_sort Xiaojun Jiang
title Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm
title_short Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm
title_full Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm
title_fullStr Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm
title_full_unstemmed Online English Teaching Course Score Analysis Based on Decision Tree Mining Algorithm
title_sort online english teaching course score analysis based on decision tree mining algorithm
publisher Hindawi-Wiley
series Complexity
issn 1099-0526
publishDate 2021-01-01
description With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors’ decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given.
url http://dx.doi.org/10.1155/2021/5577167
work_keys_str_mv AT xiaojunjiang onlineenglishteachingcoursescoreanalysisbasedondecisiontreeminingalgorithm
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